The interplay between chromatin, transcription factors and genes generates complex
regulatory circuits that can be represented as gene regulatory networks (GRNs). The study …

Over the last decade, biology has begun utilizing 'big data'approaches, resulting in large,
comprehensive atlases in modalities ranging from transcriptomics to neural connectomics …

Existing methods for gene regulatory network (GRN) inference rely on gene expression data
alone or on lower resolution bulk data. Despite the recent integration of chromatin …

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction
for the visualization and analysis of genomic data, but are limited in their interpretability: it is …

Technological development has enabled the profiling of gene expression and chromatin
accessibility from the same cell. We develop scREG, a dimension reduction methodology …

Diagonal integration of multimodal single-cell data emerges as a trending topic. However,
empowering diagonal methods for novel biological discoveries requires bridging huge gaps …

Inference of gene regulatory networks has been an active area of research for around 20
years, leading to the development of sophisticated inference algorithms based on a variety …

Cells are regulated at multiple levels, from regulations of individual genes to interactions
across multiple genes. Some recent neural network models can connect molecular changes …

Despite recent developments, it is hard to profile all multi-omics single-cell data modalities
on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations …

Leukemias are refractory hematological malignancies, characterized by marked intrinsic
heterogeneity which poses significant obstacles to effective treatment. However, traditional …